AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS
Copyright policy )AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS
Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.
Technology, Atmospheric Science, Artificial intelligence, Scale-space segmentation, Object-Based Analysis, Image Analysis, Pattern recognition (psychology), Feature Extraction, Remote Sensing, Engineering, Segmentation, Segmentation-based object categorization, Multispectral pattern recognition, Minimum spanning tree-based segmentation, Media Technology, Applied optics. Photonics, Multispectral image, Image segmentation, Object-Oriented Analysis, Ecology, T, Remote Sensing in Vegetation Monitoring and Phenology, Engineering (General). Civil engineering (General), Hyperspectral Image Analysis and Classification, Computer science, TA1501-1820, Earth and Planetary Sciences, Applications of Remote Sensing in Geoscience and Agriculture, FOS: Biological sciences, Physical Sciences, Environmental Science, Change detection, Conditional random field, Computer vision, Pixel, TA1-2040
Technology, Atmospheric Science, Artificial intelligence, Scale-space segmentation, Object-Based Analysis, Image Analysis, Pattern recognition (psychology), Feature Extraction, Remote Sensing, Engineering, Segmentation, Segmentation-based object categorization, Multispectral pattern recognition, Minimum spanning tree-based segmentation, Media Technology, Applied optics. Photonics, Multispectral image, Image segmentation, Object-Oriented Analysis, Ecology, T, Remote Sensing in Vegetation Monitoring and Phenology, Engineering (General). Civil engineering (General), Hyperspectral Image Analysis and Classification, Computer science, TA1501-1820, Earth and Planetary Sciences, Applications of Remote Sensing in Geoscience and Agriculture, FOS: Biological sciences, Physical Sciences, Environmental Science, Change detection, Conditional random field, Computer vision, Pixel, TA1-2040
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